I am currently interested in graphical models, algebraic statistics, nonparametric multivariate dependence testing, and machine learning.

Publications

Symmetric Rank Covariances: a Generalised Framework for Nonparametric Measures of Dependence
Luca Weihs, Mathias Drton, and Nicolai Meinshausen (2017)
Under review.
Learning to Predict Citation-Based Impact Measures
Luca Weihs and Oren Etzioni (2017)
Published in JCDL'17.
Determinantal Generalizations of Instrumental Variables
Luca Weihs, Bill Robinson, Emilie Dufresne, Jennifer Kenkel, et al. (2017)
Under review.
Marginal likelihood and model selection for Gaussian latent tree and forest models
Mathias Drton, Shaowei Lin, Luca Weihs, and Piotr Zwiernik (2017)
Published in Bernoulli.
Large-Sample Theory for the Bergsma-Dassios Sign Covariance
Preetam Nandy, Luca Weihs, and Mathias Drton (2016)
Published in the EJS.
Generic Identifiability of Linear Structural Equation Models by Ancestor Decomposition
Mathias Drton and Luca Weihs (2016)
Published in the Scandinavian Journal of Statistics.
Efficient Computation of the Bergsma-Dassios Sign Covariance
Luca Weihs, Mathias Drton, and Dennis Leung (2016)
Published in Computational Statistics.

Projects

Revisiting Recursive Inversion Models for Permutations
Luca Weihs (2015)
PhD Prelim Project